A Layered Overview of Agentic AI: From LLM Foundations to Multi‑Agent Systems

Agentic AI is introduced as a stacked architecture where each outer layer builds upon the inner one, adding reliability, coordination, and governance.

1. Large Language Model (LLM) Foundation Layer

The core of this layer consists of models such as GPT and DeepSeek. Key concepts include:

Tokenization and inference parameters: how text is broken into tokens and processed by the model.

Prompt engineering: designing inputs to obtain better outputs.

LLM API: programmatic interfaces that drive all downstream functionality.

2. AI Agents (Built on LLMs)

Agents wrap LLMs to give them autonomous action capabilities. Their main responsibilities are:

Tool use and function calling: connecting the LLM to external APIs or tools.

Agent reasoning: methods such as ReAct (reasoning + action) or chain‑of‑thought reasoning.

Task planning and decomposition: breaking large tasks into smaller subtasks.

Memory management: tracking history, context, and long‑term information, effectively acting as the brain that lets LLMs operate in real workflows.

3. Agentic Systems (Multi‑Agent Systems)

When multiple agents are combined, an Agentic system emerges. Its characteristics include:

Inter‑agent communication: dialogue between agents, often using protocols like ACP or A2A.

Routing and scheduling: deciding which agent handles which task and when.

State coordination: ensuring consistency across cooperating agents.

Multi‑agent RAG: applying retrieval‑augmented generation across agents.

Agent roles and specialization: agents with distinct purposes.

Orchestration frameworks: tools such as CrewAI that construct workflows and manage collaboration.

4. Agentic Infrastructure

The top layer guarantees that these systems are robust, scalable, and secure. It comprises:

Observability and logging: tracking performance and outputs (e.g., using DeepEval).

Error handling and retries: resilience to failures.

Security and access control: preventing agents from overstepping boundaries.

Rate limiting and cost management: controlling resource consumption.

Workflow automation: integrating the agents into broader pipelines.

Human‑in‑the‑loop control: allowing supervision and manual intervention, which is essential for enterprise trust and safety.

Overall, Agentic AI forms a layered stack where each successive layer adds governance, coordination, and operational safeguards on top of the underlying LLM capabilities.